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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

共 140 条记录，以下是41-50 订阅

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New Algorithms for distributed Fair k-Center Clustering: Almost Accurate as Sequential Algorithms 23

New Algorithms for Distributed Fair k-Center Clustering: Alm...

引用

23rd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoliang Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Erie United States Department of Computer Science and Engineering State University of New York at Buffalo Buffalo United States Hunan Provincial Key Lab on Bioinformatics Central South University Xiangjiang Laboratory Changsha China

Fair clustering problems have been paid lots of attention recently. In this paper, we study the k-Center problem under the group fairness and data summarization fairness constraints, denoted as Group Fair k-Center (GFkC) and Data Summarization Fair k-Center (DSFkC), respectively, in the massively parallel computational (MPC) distributed model. The previous best results for the above two problems in the MPC model are a 9-approximation with violation 7 (WWW 2022) and a (17 + ∊)-approximation without fairness violation (ICML 2020), respectively. In this paper, we obtain a (3 + ∊)- approximation with violation 1 for the GFkC problem in the MPC model, which is almost as accurate as the best known approximation ratio 3 with violation 1 for the sequential algorithm of the GFkC problem. Moreover, for the DSFkC problem in the MPC model, we obtain a (4 + ∊)-approximation without fairness violation, which is very close to the best known approximation ratio 3 for the sequential algorithm of the DSFkC problem. Empirical experiments show that our distributed algorithms perform better than existing state-of-the-art distributed methods for the above two problems. © 2024 international Foundation for Autonomous Agents and Multiagent Systems.

关键词： Machine learning

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international Program Committee

International Program Committee

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

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Message from conference Chairs

Message from Conference Chairs

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

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BRB-based distributed fault diagnosis for consensus of heterogeneous multi-agent systems 23

BRB-based distributed fault diagnosis for consensus of heter...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Ruohan Feng, Zhichao School of Electronics and Information Northwestern Polytechnical University Xi'an China High-Tech Institute of Xi'an Xi'an China Harbin Institute of Technology Harbin China

ISBN: (纸本)9781665494571

This paper studies the belief rule base (BRB) expert system based distributed fault diagnosis problem for a group of heterogeneous multi-agent systems (MASs) subject to unpredictable disturbances. First, a novel distributed consensus protocol is developed based on the feedforward approach and internal reference model under directed topology. It is shown that under the proposed consensus protocol, the MASs without fault could achieve bounded consensus asymptotically. Then, novel distributed fault diagnosis models based on BRB are developed for each agent by using the output information of neighbor agents. It should be noted that BRB can handle the information with uncertainty and ambiguity that can address the unpredictable interference in engineering practice. Thus, the proposed BRB based distributed fault diagnosis models for MASs can diagnose the fault and meanwhile eliminate the influence of the unpredictable interference in engineering practice. Finally, a simulation example is provided to illustrate the effectiveness of the approach proposed in this paper. © 2021 IEEE.

关键词： Fault diagnosis Uncertainty Smart cities Interference Consensus protocol Topology Feedforward systems

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Message from the conference Chair

Message from the Conference Chair

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Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD-Summer), ACIS international

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Query Performance Comparison of PostgreSQL vs. Neo4j. A Basic distributed Setup on OpenStack 23

Query Performance Comparison of PostgreSQL vs. Neo4j. A Basi...

引用

23rd RoEduNet conference: networking in Education and Research, RoEduNet 2024

作者： Fotache, Marin Teaca, Nicoleta Esanu, Codrin-Stefan Cluci, Marius-Iulian Pinzaru, Ciprian Gasner, Paul Cuza University of Iasi Alexandru Ioan Dept. of Accounting Information Systems and Statistics Iasi Romania Cuza University of Iasi Alexandru Ioan Iasi Romania Cuza University of Iasi Cegeka Romania Iasi Alexandru Ioan Romania Cuza University of Iasi S.C. HVM SRL IaşIasi Alexandru Ioan Romania Cuza University of Iasi Faculty of Physics Alexandru Ioan Iasi Romania

ISBN: (纸本)9798331540388

Among the NoSQL technologies, Neo4j is one of the most popular solutions for managing graph databases and an early adopter of transactions (contrary to other NoSQL Systems). Neo4j also provides a powerful high-level data processing language-Cypher. Despite its popularity, there are few comprehensive studies about benchmarking the query performance of Neo4j relative to SQL or other NoSQL counterparts. In this paper, we present a module for converting the TPC-H benchmark database from PostgreSQL to Neo4j, and we built a set of 110 SQL queries that were translated into Cypher. For both database servers, the queries were executed with a 10-minute timeout on OpenStack setups following nine scenarios by combining three database scale factors (1 GB, 5 mGB, and 10 GB) with three data distribution variants (with 1,5, and 10 nodes). Results provide support for query performance assessment of these two big data products. © 2024 IEEE.

关键词： Structured Query Language

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distributed Parallel Tall-Skinny QR Factorization: Performance Evaluation of Various Algorithms on Various Systems 23rd

Distributed Parallel Tall-Skinny QR Factorization: Performan...

引用

23rd international conference on Parallel and distributed computing, Applications, and Technologies, PDCAT 2022

作者： Fukaya, Takeshi Information Initiative Center Hokkaido University Sapporo Japan JST Presto Tokyo Japan

ISBN: (纸本)9783031299261

In this paper, we focus on the distributed parallel computation of tall-skinny QR factorization. Among various numerical algorithms, we evaluate the performance of four typical algorithms that have different characteristics on four different supercomputer systems. Together with a brief summary of each algorithm, this paper presents the current situation of the distributed parallel tall-skinny QR factorization, which will help applications to select an appropriate numerical algorithm for tall-skinny QR factorization. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supercomputers

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Research of Seismic Streaming Storage Based on Druid 23

Research of Seismic Streaming Storage Based on Druid

引用

作者： Gao, Bing Chen, Jie Zou, Qijie Qin, Jing School of Information Engineering Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China School of Software Engineerin Dalian University Dalian Key Laboratory of Smart Medical and Health Dalian China

ISBN: (纸本)9781665494571

Along with the growth of seismic observation networks, the amount of seismic observation data is also increasing rapidly with the form of fast arrival stream, and it is an important and challenging task to store seismic data in time. However, two bottlenecks (slow read/write speed, poor user concurrency) still make the practical applications difficult. We proposes an Apache Druid-based storage solution for seismic observation data streams. The solution is more adaptable to the read/write requirement of large-scale seismic data streams. Specifically, we achieve real-time observation for two aspects: (I) using Kafka as a distributed message exchange system;and (II) implementing accesses and exchange with massive seismic stream data onto the Kafka indexing service provided by Druid. The experimental results show that the Druid-based temporal data storage solution has better stream input and real-time query performance compared with the existed Hbase storage method. © 2021 IEEE.

关键词： Concurrent computing Smart cities Time series analysis Memory distributed databases Real-time systems Task analysis

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distributed Task Offloading for IoAV Using DDP-DQN 1

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23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Peng, Xiting Li, Wenjie Zhang, Xiaoyu Dong, Mianxiong Ota, Kaoru Song, Shun Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Shenyang Key Lab Informat Percept & Edge Comp Shenyang Peoples R China Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido 0508585 Japan

ISBN: (数字)9789819708116

ISBN: (纸本)9789819708109;9789819708116

The swift progress of the Internet of Vehicles (IoV) and autonomous driving technology has facilitated the emergence of the Internet of Autonomous Vehicles (IoAV). If delay-sensitive vehicle tasks are not completed on time, it will lead to bad consequences for IoAV. Task offloading technology can solve the problem that the vehicle cannot meet the task requirements. However, highly dynamic vehicle networks and diverse vehicle applications require more intelligent task offloading strategies. Therefore, this paper addresses the distributed task offloading problem in the IoAV to meet diverse vehicle task demands. First, we model the vehicle task offloading problem as a decision problem, and a deep reinforcement learning (DRL) algorithm named DDP-DQN (double-dueling-prioritize-DQN) is applied to complete vehicle tasks more efficiently. Then, we design a reward function to complete the task within the acceptable maximum delay of the task while reducing the consumption of resources. Simulations demonstrate the outperforming of the DDP-DQN compared with other three reinforcement learning algorithms.

关键词： Internet of autonomous vehicles Task offloading Vehicle edge computing Deep reinforcement learning

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PipePrune: Pipeline Parallel Based on Convolutional Layer Pruning for distributed Deep Learning 23

PipePrune: Pipeline Parallel Based on Convolutional Layer Pr...

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作者： Tan, Daojun Jiang, Wenbin Qin, Shang Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technol. and Syst. Serv. Comp. Technol. and Syst. Lab Cluster and Grid Comp. Lab Wuhan430074 China

ISBN: (纸本)9781665494571

Benefitting from the combination of the idea of pipeline with model parallelism and data parallelism, pipeline parallelism improves the efficiency of distributed deep learning systems significantly. However, suffering from the bubbles and gaps caused by the imbalance of different stages in pipeline, it can not output ideal performance yet. To explore the potential of pipeline parallelism further, we propose a novel approach called PipePrune, which adds a convolutional layer pruning strategy to pipeline creatively to reduce the bubbles and gaps. For the convolutional layers with heavy overheads, some unimportant kernels are pruned by the measurement of the L1-norm. This approach makes the processing overheads of different pipeline stages more balanced. The experimental results show that, compared with state-of-the-art pipeline methods, PipePrune can improve the training speeds obviously (e.g. for ResNet50 on ImageNet, about 30% speed improvement is realized with only 1.1% loss of training accuracies). © 2021 IEEE.

关键词： Training Deep learning Smart cities Pipelines Neural networks distributed databases Parallel processing

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